Demonstrate the relationships between nurse staffing and patient outcomes in acute care settings.

Describe decision support software prototype designed to support nurse staffing decision making.

Describe technology opportunities and challenges when implementing a decision-support dashboard.

Concern for the quality of patient care in hospitals and the impact of adverse outcomes on costs remain formidable problems in the United States (U.S.) healthcare system. Unfortunately, patients who experience adverse events can have additional days of hospitalization, develop permanent disability, or die. In most cases, adverse events are believed to be avoidable, but prevention of these events is usually dependent on nurses, especially registered nurses. It is the role of nurse leaders to provide a sufficient number and the right mix of nurses in order to anticipate problems and implement measures to reduce the probability of an adverse event. Landmark research in the early 2000’s demonstrated significant relationships between nurse staffing in hospitals and patient outcomes. On-going research and reviews have continued to support the relationships. However, there is a gap between the research findings and translation into practice. Technology offers a means to bridge this gap and provide nurse leaders with the ability to base staffing decisions on real-time, credible evidence.

The purpose of this presentation is to describe the translation of nurse staffing research findings conducted in acute care hospitals in a large healthcare system into decision support software. Data at hospital, unit, and patient levels were examined using hierarchical linear modeling. Significant predictive relationships between nurse staffing and patient outcomes were identified. The research indicated that higher hours of registered nurse (RNs) care per equivalent patient day and higher percentages of RNs in the skill mix are significantly related to lower mortality, numbers of adverse events, shorter lengths of stay, and fewer medication errors, falls, and other patient care errors. In some cases, higher licensed practical nurses (LPNs) and unlicensed assistive personnel (UAPs) hours and percentages in the skill mix were predictive of higher errors.
The research results have been incorporated into a prototype decision support software designed to support nurse leaders in staffing decisions. Data from multiple organizational databases are integrated into the software providing nurse leaders with the most current information on nursing staff and patients on their units. Predictive equations based on the research findings are incorporated into the software to forecast patient outcomes given the current conditions on the unit. Nurse leaders can explore staffing options and the resulting outcomes in a scenario mode.

The decision support software provides nurse leaders with critical information at the point of staffing decisions. This approach replaces antiquated, retrospective monthly reporting about patient outcomes. Use of the dashboard will enable nurse leaders to allocate human resources in ways that minimize costs while avoiding staffing decisions that compromise quality outcomes. This presentation will provide an overview of nursing decision support software, the expansion of cloud computing in nursing, and specifically discuss the results of our studies in this area.